epoc.svd(model, k=1, C=1, numload=NULL)
epoc.survival(G.svd, Y, U, surv, C=1, type=NULL)
epoc.svdplot(G.svd, C=1)
## S3 method for class 'EPoC.survival'
plot(x,...)
## S3 method for class 'EPoC.survival'
summary(object,...)
## S3 method for class 'summary.EPoC.survival'
print(x,...)
Arguments
model
An object from epocG or epocA or a Matrix from epoc.bootstrap and friends.
k
In case model come from epocG or epocA select a model of sparsity level k in [1,K]. The default k=1 means first/most sparse.
C
Default 1. For epoc.svd the number of components. For epoc.survival and epoc.svdplot, which component to use.
numload
Number of loadings in the sparse components, a vector for each component. Default 10 for all components.
G.svd
The list obtained from epoc.svd.
Y
mRNA, samples x genes.
U
CNA, samples x genes.
surv
Survival data for the samples.
type
'G' means EPoC G and 'A' means EPoC A.
x
An object from epoc.survival
object
An object from epoc.survival
...
Parameters passed down to underlying functions, plot.default for plot and print.default for print.
Details
Applies survival analysis using the first SVD component, but other components can also be used by changing the input value of C. Survival scores are generated as described in Subsect. 2.4 in the second paper referenced. A simple non-parametric survival analysis is performed, comparing survival between patientswith positive or negative scores (tumor fitness).
Value
The epoc.survival object contains the summary information from a log-rank test comparing survival (survdiff) and survival fit objects.